dc.creator | Denecke, Kerstin | es |
dc.creator | May, Richard | es |
dc.creator | Rivera Romero, Octavio | es |
dc.date.accessioned | 2023-11-23T12:19:48Z | |
dc.date.available | 2023-11-23T12:19:48Z | |
dc.date.issued | 2023-10 | |
dc.identifier.citation | Denecke, K., May, R. y Rivera Romero, O. (2023). How Can Transformer Models Shape Future Healthcare: A Qualitative Study. Studies in Health Technology and Informatics, 43-47. https://doi.org/10.3233/SHTI230736. | |
dc.identifier.issn | 0926-9630 | es |
dc.identifier.issn | 1879-8365 | es |
dc.identifier.uri | https://hdl.handle.net/11441/151430 | |
dc.description.abstract | Transformer models have been successfully applied to various natural
language processing and machine translation tasks in recent years, e.g. automatic
language understanding. With the advent of more efficient and reliable models (e.g.
GPT-3), there is a growing potential for automating time-consuming tasks that could
be of particular benefit in healthcare to improve clinical outcomes. This paper aims
at summarizing potential use cases of transformer models for future healthcare
applications. Precisely, we conducted a survey asking experts on their ideas and
reflections for future use cases. We received 28 responses, analyzed using an
adapted thematic analysis. Overall, 8 use case categories were identified including
documentation and clinical coding, workflow and healthcare services, decision
support, knowledge management, interaction support, patient education, health
management, and public health monitoring. Future research should consider
developing and testing the application of transformer models for such use cases. | es |
dc.format | application/pdf | es |
dc.format.extent | 5 p. | es |
dc.language.iso | eng | es |
dc.publisher | IOS Press | es |
dc.relation.ispartof | Studies in Health Technology and Informatics, 43-47. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Transformer models | es |
dc.subject | Deep learning | es |
dc.subject | Healthcare | es |
dc.subject | Applications | es |
dc.title | How Can Transformer Models Shape Future Healthcare: A Qualitative Study | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.publisherversion | https://ebooks.iospress.nl/doi/10.3233/SHTI230736 | es |
dc.identifier.doi | 10.3233/SHTI230736 | es |
dc.contributor.group | Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial | es |
dc.journaltitle | Studies in Health Technology and Informatics | es |
dc.publication.initialPage | 43 | es |
dc.publication.endPage | 47 | es |